Explainable identification and mapping of trees using UAV RGB image and deep learning

The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are costly and are thus unsuitable for small-scale forest managers. In this work, we constructed a machine vision system for tree identification and mapping using Red-Green-Blue (RGB) image taken by an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN). In this system, we first calculated the slope from the three-dimensional model obtained by the UAV, and segmented the UAV RGB photograph of the forest into several tree crown objects automatically using colour and three-dimensional information and the slope model, and lastly applied object-based CNN classification for each crown image. This system succeeded in classifying seven tree classes, including several tree species with more than 90% accuracy. The guided gradient-weighted class activation mapping (Guided Grad-CAM) showed that the CNN classified trees according to their shapes and leaf contrasts, which enhances the potential of the system for classifying individual trees with similar colours in a cost-effective manner-a useful feature for forest management.

Automated summary

The study developed a machine vision system for tree identification and mapping using RGB images captured by a UAV and CNN. The system successfully classified seven tree classes with over 90% accuracy, and the Guided Grad-CAM analysis showed that the CNN classified trees based on their shapes and leaf contrasts, enhancing the system's potential for cost-effective classification of individual trees with similar colors.